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A Method for Optimum Control of Dynamic Load Distribution in Time-sensitive Communication Networks for Manufacturing Automation

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Weichlein, Thomas (2024) A Method for Optimum Control of Dynamic Load Distribution in Time-sensitive Communication Networks for Manufacturing Automation. PhD thesis, University of Gloucestershire.

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Official URL: https://eprints.glos.ac.uk/14198/

Abstract

Communication between end stations in a contemporary communication network typically occurs over several paths for media redundancy and throughput enhancement. To use all paths as evenly as possible, load balancing or load distribution methods are applied, mainly in higher level information technology (IT) networks such as Internet Service Provider (ISP) networks, campus networks, and mobile access networks. Automation networks of operations technology (OT), particularly Manufacturing Automation Networks (MAN), have rarely been subject to load distribution control. IEEE Time Sensitive Networks (TSN) are a relatively young network development that offers, among other features, redundant paths for automation networks, the essential precondition for load distribution. Furthermore, the TSN framework defines several traffic shapers and schedulers which are expected to have different impacts on Load Distribution Control (LDC). However, the selection of the right traffic shaper or scheduler for an automation network is challenging. Their influence depends on various network parameters such as network extension, network cycles, application cycles, and the amount of data per traffic class and per network cycle. This thesis proposes, designs, and develops a method for optimum control of dynamic load distribution in time-sensitive communication networks for manufacturing automation. The research philosophy underlying this research project is positivism. Literature review (textual analysis) is used to obtain secondary data on relevant use cases of automation communication, control theory concepts, and network standards. To obtain the primary data on the control results, simulation is used. Firstly, the influence of different TSN MAN network parameters and properties on LDC is investigated. Secondly, the data flow control is analysed as a subsequent control task for LDC under the influence of the different traffic shapers and traffic schedulers. Based on these results, thirdly, a dedicated optimum load distribution control method for MAN with a single automation controller (AC) is proposed. Then, this optimised LDC method is applied to a network with multiple ACs. Contribution: The results of the analysis and evaluation of the influence of the various automation parameters as well as the TSN shapers and schedulers provide a detailed picture of the data flow control options within TSN MAN. The derivation of control properties for data flow control and load distribution control as well as their control simulation and evaluation of the results create the prerequisites for the design of LDC solutions in these networks. A strong influence of the application cycles on the control dynamics and stability is demonstrated. It is further shown that network nodes using SPQ, SPQ with Preemption, and EST provide rather low path delays. They are the best shaper and scheduler selections in high dynamic networks and in larger networks. The application of network nodes using CQF and ATS can result in significantly high path delay times, and thus, high control dead times, especially in larger networks with a high hop count. Therefore, they are recommended only for smaller network sizes with lower dynamic requirements. Based on this preliminary work, the study also provides optimised control solutions for single and multiple AC LDCs, both with and without mutual AC interference. The results based on network simulations confirm the suitability of these solutions and show that the overall convergence time improves. Thus, the present study provides a new comprehensive view and solutions to the possibilities of LDC within TSN MAN that have been lacking in the research literature so far.

Item Type:

Thesis (PhD)

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